An Efficient Algorithm for Depression Filling and Flat-Surface Processing in Raster DEMs

Depressions (or pits) and flat surfaces (or flats) are general types of terrain in raster digital elevation models (DEMs). Depressions are lower areas surrounded by terrain without outlets, and flat surfaces are areas with no local gradient. To extract hydrologic or geomorphic properties from DEMs, these two types of terrain need to be addressed. This letter presents an efficient algorithm for filling depressions and for adding increments to flat surfaces. The algorithm builds on previous work, offering several important improvements. The improved algorithm uses two queues: a priority queue and a “first-in, first-out” (FIFO) queue. The FIFO queue is used to process depressions and flat surfaces, and the priority queue processes other terrain. The improved algorithm achieves an O(M log2 M) time complexity, where M is less than the total number of cells, which is more efficient than the algorithm proposed by Wang and Liu. In addition, the improved algorithm not only fills depressions but also elevates flat surfaces for the convenience of extracting flow directions. Furthermore, to adapt to different data types, for example, integer, single-precision floating point, and double precision, the improved algorithm does not alter flat-surface elevations in DEMs directly but uses a mask matrix to mark the incremental elevation values of flat surfaces. In speed comparison testing, the improved algorithm performed up to 16%-32% faster than the original.

[1]  Frédéric Darboux,et al.  A fast, simple and versatile algorithm to fill the depressions of digital elevation models , 2002 .

[2]  Zhang Yeting The Extraction of Catchment and Subcatchment from Regular Grid DEMs , 2005 .

[3]  David M. Mark,et al.  Part 4: Mathematical, Algorithmic and Data Structure Issues: Automated Detection Of Drainage Networks From Digital Elevation Models , 1984 .

[4]  Dayong Shen,et al.  Area Partitioning for Channel Network Extraction Using Digital Elevation Models and Remote Sensing , 2012, IEEE Geoscience and Remote Sensing Letters.

[5]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[6]  Zhang Wan-chang,et al.  A new method for treating the depressions and flat areas in DEM for large-scale hydrology and climate models , 2007 .

[7]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[8]  Song Xiaomeng Advances in digital watershed features extracting based on DEM , 2013 .

[9]  Jon D. Pelletier,et al.  A robust, two‐parameter method for the extraction of drainage networks from high‐resolution digital elevation models (DEMs): Evaluation using synthetic and real‐world DEMs , 2013 .

[10]  L. Wang,et al.  An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling , 2006, Int. J. Geogr. Inf. Sci..

[11]  Andrea Tribe,et al.  Automated recognition of valley lines and drainage networks from grid digital elevation models: a review and a new method , 1992 .

[12]  Kwan Tun Lee,et al.  An efficient method for DEM-based overland flow routing , 2013 .

[13]  J. Lindsay,et al.  Removal of artifact depressions from digital elevation models: towards a minimum impact approach , 2005 .